A machine learning model using 11 predictors demonstrated good discrimination (c-statistic 0.760) for identifying acute heart failure patients at high risk for non-home discharge.
Cohort (n=128,068)
Yes
Does a machine-learning-based prediction model using 11 clinical variables accurately predict non-home discharge or in-hospital death in patients admitted for acute heart failure?
A parsimonious 11-variable machine learning model can accurately predict the risk of non-home discharge in patients hospitalized for acute heart failure, aiding in early care coordination.
Effect estimate: c-statistic 0.760 (95% CI 0.752-0.767)
BACKGROUND: Scarce data on factors related to discharge disposition in patients hospitalized for acute heart failure (AHF) were available, and we sought to develop a parsimonious and simple predictive model for non-home discharge via machine learning. METHODS: This observational cohort study using a Japanese national database included 128,068 patients admitted from home for AHF between April 2014 and March 2018. The candidate predictors for non-home discharge were patient demographics, comorbidities, and treatment performed within 2 days after hospital admission. We used 80% of the population to develop a model using all 26 candidate variables and using the variable selected by 1 standard-error rule of Lasso regression, which enhances interpretability, and 20% to validate the predictive ability. RESULTS: We analyzed 128,068 patients, and 22,330 patients were not discharged to home; 7,879 underwent in-hospital death and 14,451 were transferred to other facilities. The machine-learning-based model consisted of 11 predictors, showing a discrimination ability comparable to that using all the 26 variables (c-statistic: 0.760 95% confidence interval, 0.752-0.767 vs. 0.761 95% confidence interval, 0.753-0.769). The common 1SE-selected variables identified throughout all analyses were low scores in activities of daily living, advanced age, absence of hypertension, impaired consciousness, failure to initiate enteral alimentation within 2 days and low body weight. CONCLUSIONS: The developed machine learning model using 11 predictors had a good predictive ability to identify patients at high risk for non-home discharge. Our findings would contribute to the effective care coordination in this era when HF is rapidly increasing in prevalence.
Okada et al. (Wed,) conducted a cohort in Acute heart failure (n=128,068). 11-predictor machine learning model vs. 26-variable model was evaluated on Non-home discharge (transfer to other facilities or in-hospital death) (c-statistic 0.760, 95% CI 0.752-0.767). A machine learning model using 11 predictors demonstrated good discrimination (c-statistic 0.760) for identifying acute heart failure patients at high risk for non-home discharge.